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Record W2562706198 · doi:10.1037/xlm0000344

Reading aloud: On the determinants of the joint effects of stimulus quality and word frequency.

2016· article· en· W2562706198 on OpenAlexfundno aff
Darcy White, Derek Besner

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2016
Typearticle
Languageen
FieldPsychology
TopicReading and Literacy Development
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWord lists by frequencyOrthographyStimulus (psychology)PsychologyPhonologyRead aloudReading aloudWord recognitionCognitive psychologySpeech recognitionCommunicationLinguisticsComputer scienceNatural language processingReading (process)

Abstract

fetched live from OpenAlex

There are multiple reports, in the context of the time taken to read aloud, that the joint effects of stimulus quality and word frequency (a) interact when only words appear in the list but (b) are additive when nonwords are intermixed with words (O'Malley & Besner, 2008). This triple interaction has been explained in terms of the idea that different processing modes are in play in these different contexts. Processing is cascaded when only words appear in the list, allowing the effect of stimulus quality to influence the downstream process(es) affected by word frequency. In contrast, when nonwords appear in the list an early process affected by stimulus quality, but not word frequency, is staged (thresholded) so as to reduce the probability of lexicalizations (reading a nonword as a word) when stimulus quality is low. The present experiment addresses the issue of whether such thresholding in the presence of nonwords is driven by the orthography or phonology of the nonwords included in the stimulus set. Participants read words aloud that varied in word frequency and were randomly intermixed with nonwords that all sounded identical to words (e.g., BRANE for BRAIN). Stimulus quality and word frequency had additive effects on the time to read aloud in this context, consistent with the view that it is the orthography of the nonwords that matters. Other aspects of the results suggest that between level feed-back is in play when this particular kind of nonword is used. (PsycINFO Database Record

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.367
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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